{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "2de5dd1f",
      "metadata": {
        "id": "2de5dd1f"
      },
      "source": [
        "### Train a VLM in Google Colab!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "OCooV08mNANR",
      "metadata": {
        "id": "OCooV08mNANR"
      },
      "source": [
        "### Clone the repository if you don't have it already"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ooQMjmrMLn-4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ooQMjmrMLn-4",
        "outputId": "8df3283d-79af-425e-bb51-5c73242c3c0c"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "if not os.path.isdir('nanoVLM'):\n",
        "    !git clone https://github.com/huggingface/nanoVLM.git\n",
        "%cd nanoVLM/\n",
        "!ls"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "mMhc9OCENup5",
      "metadata": {
        "id": "mMhc9OCENup5"
      },
      "source": [
        "### Imports and Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bcw8qQqoOSR7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcw8qQqoOSR7",
        "outputId": "9990c51c-24f6-4173-a232-51876a79b809"
      },
      "outputs": [],
      "source": [
        "# If you get an \"Error\" from pip's dependency resolver but the cell completes fine, this is not an issue, you can continue :)\n",
        "!pip -q install torch\n",
        "!pip -q install gcsfs\n",
        "!pip -q install datasets==3.5.0\n",
        "!pip -q install tqdm\n",
        "!pip -q install huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "54bc8463",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Let's authentificate with the Hugging Face Hub so you can push your model\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5e8dc5ba",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Decide on the name of your model here!\n",
        "# You will need your HF user name and the name you want to give to it\n",
        "# For me, this would be \"lusxvr/nanoVLM\"\n",
        "hf_model_name = \"YOUR-HF-USERNAME/nanoVLM\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "OTsl1jZrMeaJ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTsl1jZrMeaJ",
        "outputId": "ca72280e-3666-4c84-a0b9-ac9bdaa4a512"
      },
      "outputs": [],
      "source": [
        "# nanoVLM Imports (please check out the implementations in detail, that's where all the interesting stuff is!)\n",
        "from data.datasets import VQADataset\n",
        "from data.collators import VQACollator\n",
        "from data.data_utils import synchronized_dataloader_step\n",
        "from data.advanced_datasets import ConstantLengthDataset\n",
        "from data.processors import get_image_processor, get_tokenizer\n",
        "\n",
        "import models.config as config\n",
        "from models.vision_language_model import VisionLanguageModel\n",
        "\n",
        "# Libraries\n",
        "import math\n",
        "import time\n",
        "import torch\n",
        "from tqdm import tqdm\n",
        "import torch.optim as optim\n",
        "import matplotlib.pyplot as plt\n",
        "from dataclasses import dataclass, field\n",
        "from torch.utils.data import DataLoader\n",
        "from datasets import load_dataset, concatenate_datasets, get_dataset_config_names\n",
        "\n",
        "#Otherwise, the tokenizer will through a warning\n",
        "import os\n",
        "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    device = \"cuda\"\n",
        "elif hasattr(torch.backends, \"mps\") and torch.backends.mps.is_available():\n",
        "    device = \"mps\"\n",
        "else:\n",
        "    device = \"cpu\"\n",
        "print(f\"Using device: {device}\")\n",
        "\n",
        "torch.manual_seed(0)\n",
        "torch.cuda.manual_seed_all(0)\n",
        "\n",
        "# To reload the modules if you change something in the code\n",
        "%reload_ext autoreload\n",
        "%autoreload 2"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4Vzo03IzN3Zf",
      "metadata": {
        "id": "4Vzo03IzN3Zf"
      },
      "source": [
        "### Get the dataloaders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3Zzn2FI2N7Aj",
      "metadata": {
        "id": "3Zzn2FI2N7Aj"
      },
      "outputs": [],
      "source": [
        "def get_dataloaders(train_cfg, vlm_cfg):\n",
        "    # Create datasets\n",
        "    image_processor = get_image_processor(vlm_cfg.max_img_size, vlm_cfg.vit_img_size, vlm_cfg.resize_to_max_side_len)\n",
        "    tokenizer = get_tokenizer(vlm_cfg.lm_tokenizer, vlm_cfg.vlm_extra_tokens, vlm_cfg.lm_chat_template)\n",
        "\n",
        "    # Load and combine all training datasets\n",
        "    dataset_names_to_load = train_cfg.train_dataset_name\n",
        "    if \"all\" in dataset_names_to_load:\n",
        "        dataset_names_to_load = get_dataset_config_names(train_cfg.train_dataset_path)\n",
        "\n",
        "    combined_train_data = []\n",
        "\n",
        "    for dataset_name in dataset_names_to_load:\n",
        "        print(f\"Loading dataset: {dataset_name}\")\n",
        "        try:\n",
        "            train_ds = load_dataset(train_cfg.train_dataset_path, dataset_name)['train']\n",
        "            train_ds[0] # Check if the dataset is loaded correctly\n",
        "            combined_train_data.append(train_ds)\n",
        "        except Exception as e:\n",
        "            print(f\"Warning: Failed to load dataset config '{dataset_name}' from '{train_cfg.train_dataset_path}'. Error: {e}\")\n",
        "            continue\n",
        "    train_ds = concatenate_datasets(combined_train_data)\n",
        "    \n",
        "    train_ds = train_ds.shuffle(seed=0) # Shuffle the training dataset, so train and val get equal contributions from all concatenated datasets\n",
        "\n",
        "    # Apply cutoff if specified\n",
        "    if train_cfg.data_cutoff_idx is None:\n",
        "        total_samples = len(train_ds)  # Use the entire dataset\n",
        "    else:\n",
        "        total_samples = min(len(train_ds), train_cfg.data_cutoff_idx)\n",
        "\n",
        "    val_size = int(total_samples * train_cfg.val_ratio)\n",
        "    train_size = total_samples - val_size\n",
        "\n",
        "    val_ds = train_ds.select(range(train_size, total_samples-1))\n",
        "    train_ds = train_ds.select(range(train_size))\n",
        "\n",
        "    train_dataset = VQADataset(train_ds, tokenizer, image_processor, vlm_cfg.mp_image_token_length)\n",
        "    val_dataset = VQADataset(val_ds, tokenizer, image_processor, vlm_cfg.mp_image_token_length)\n",
        "\n",
        "    train_dataset = ConstantLengthDataset(train_dataset, infinite=False, max_sample_length=train_cfg.max_sample_length, seq_length=vlm_cfg.lm_max_length, num_of_sequences=train_cfg.batch_size*4, queue_size=8,\n",
        "                                        max_images_per_example=train_cfg.max_images_per_example, max_images_per_knapsack=train_cfg.max_images_per_knapsack)\n",
        "\n",
        "    # Create collators\n",
        "    vqa_collator = VQACollator(tokenizer, vlm_cfg.lm_max_length)\n",
        "\n",
        "    # Create dataloaders\n",
        "\n",
        "    train_loader = DataLoader(\n",
        "        train_dataset,\n",
        "        batch_size=train_cfg.batch_size,    # =per device BS in DDP\n",
        "        collate_fn=vqa_collator,\n",
        "        num_workers=1,\n",
        "        pin_memory=True,\n",
        "        persistent_workers=True,\n",
        "        drop_last=True,\n",
        "    )\n",
        "\n",
        "    val_loader = DataLoader(\n",
        "        val_dataset,\n",
        "        batch_size=train_cfg.batch_size,\n",
        "        shuffle=False,\n",
        "        collate_fn=vqa_collator,\n",
        "        num_workers=1,\n",
        "        pin_memory=True,\n",
        "        persistent_workers=True,\n",
        "        drop_last=True,\n",
        "    )\n",
        "\n",
        "    # Warmup dataloaders to kickstart worker processes\n",
        "    print(\"Warming up dataloaders...\")   \n",
        "    next(iter(train_loader))\n",
        "    next(iter(val_loader))\n",
        "    print(\"Warmup complete.\")\n",
        "\n",
        "    return train_loader, val_loader"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "_F8u3MJ6PAfd",
      "metadata": {
        "id": "_F8u3MJ6PAfd"
      },
      "source": [
        "### Prepare the training loop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "KxOtMU5zPD-4",
      "metadata": {
        "id": "KxOtMU5zPD-4"
      },
      "outputs": [],
      "source": [
        "def get_lr(it, max_lr, max_steps):\n",
        "    min_lr = max_lr * 0.1\n",
        "    warmup_steps = max_steps * 0.03\n",
        "    # 1) linear warmup for warmup_iters steps\n",
        "    if it < warmup_steps:\n",
        "        return max_lr * (it+1) / warmup_steps\n",
        "    # 2) if it > lr_decay_iters, return min learning rate\n",
        "    if it > max_steps:\n",
        "        return min_lr\n",
        "    # 3) in between, use cosine decay down to min learning rate\n",
        "    decay_ratio = (it - warmup_steps) / (max_steps - warmup_steps)\n",
        "    assert 0 <= decay_ratio <= 1\n",
        "    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff starts at 1 and goes to 0\n",
        "    return min_lr + coeff * (max_lr - min_lr)\n",
        "\n",
        "def train(train_cfg, vlm_cfg):\n",
        "    train_loader, val_loader = get_dataloaders(train_cfg, vlm_cfg)\n",
        "\n",
        "    # Initialize model\n",
        "    if train_cfg.resume_from_vlm_checkpoint:\n",
        "        print(f\"Resuming from VLM checkpoint: {vlm_cfg.vlm_checkpoint_path}\")\n",
        "        model = VisionLanguageModel.from_pretrained(vlm_cfg.vlm_checkpoint_path)\n",
        "    else:\n",
        "        model = VisionLanguageModel(vlm_cfg, load_backbone=vlm_cfg.vlm_load_backbone_weights)\n",
        "\n",
        "    print(f\"nanoVLM initialized with {sum(p.numel() for p in model.parameters()):,} parameters\")\n",
        "    print(f\"Training summary: {len(train_loader.dataset)} samples, {len(train_loader)} batches/epoch, batch size {train_cfg.batch_size}\")\n",
        "\n",
        "    # Define optimizer groups\n",
        "    # Since we have pretrained vision and language backbones, but a newly initialized modality projection layer, it doesn't make sense to train them with the same learning rate\n",
        "    # You could opt to fully freeze the backbones and only train the MP layer, but finetuning them with a lower learning rate makes the training as a whole easier\n",
        "    param_groups = []\n",
        "    if train_cfg.lr_mp > 0:\n",
        "        param_groups.append({'params': list(model.MP.parameters()), 'lr': train_cfg.lr_mp})\n",
        "    else:\n",
        "        for p in list(model.MP.parameters()):\n",
        "            p.requires_grad = False\n",
        "    if train_cfg.lr_vision_backbone > 0:\n",
        "        param_groups.append({'params': list(model.vision_encoder.parameters()), 'lr': train_cfg.lr_vision_backbone})\n",
        "    else:\n",
        "        for p in list(model.vision_encoder.parameters()):\n",
        "            p.requires_grad = False\n",
        "    if train_cfg.lr_language_backbone > 0:\n",
        "        param_groups.append({'params': list(model.decoder.parameters()), 'lr': train_cfg.lr_language_backbone})\n",
        "    else:\n",
        "        for p in list(model.decoder.parameters()):\n",
        "            p.requires_grad = False\n",
        "\n",
        "    optimizer = optim.AdamW(param_groups)\n",
        "    all_params = [p for group in optimizer.param_groups for p in group['params']]\n",
        "\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    model.to(device)\n",
        "    if train_cfg.compile:\n",
        "        model = torch.compile(model)\n",
        "\n",
        "    epoch_times = []\n",
        "    batch_losses = []\n",
        "    val_losses = []\n",
        "    val_plot_steps = []\n",
        "    global_step = 0\n",
        "    epoch = 0\n",
        "\n",
        "    while global_step < train_cfg.max_training_steps:\n",
        "        epoch_start_time = time.time()\n",
        "        epoch += 1\n",
        "        model.train()\n",
        "        total_train_loss = 0\n",
        "        total_tokens_processed = 0\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        print(\"Starting training loop\")\n",
        "        for i, batch in enumerate(synchronized_dataloader_step(train_loader, False)):\n",
        "            batch_start_time = time.time()\n",
        "            is_update_step = (i + 1) % train_cfg.gradient_accumulation_steps == 0 or i + 1 == len(train_loader)\n",
        "            images = batch[\"images\"]\n",
        "            input_ids = batch[\"input_ids\"].to(device)\n",
        "            labels = batch[\"labels\"].to(device)\n",
        "            attention_mask = batch[\"attention_mask\"].to(device)\n",
        "\n",
        "            with torch.autocast(device_type='cuda', dtype=torch.float16): # Mixed precision training\n",
        "                _, loss = model(input_ids, images, attention_mask=attention_mask, targets=labels)\n",
        "\n",
        "            if train_cfg.gradient_accumulation_steps > 1:\n",
        "                loss = loss / train_cfg.gradient_accumulation_steps\n",
        "\n",
        "            loss.backward()\n",
        "\n",
        "            if is_update_step:\n",
        "                if train_cfg.max_grad_norm is not None:\n",
        "                    _ = torch.nn.utils.clip_grad_norm_(all_params, max_norm=train_cfg.max_grad_norm)\n",
        "\n",
        "                param_group_idx = 0\n",
        "                if train_cfg.lr_mp > 0:\n",
        "                    adj_lr_mp = get_lr(global_step, train_cfg.lr_mp, train_cfg.max_training_steps)\n",
        "                    optimizer.param_groups[param_group_idx]['lr'] = adj_lr_mp\n",
        "                    param_group_idx += 1\n",
        "\n",
        "                if train_cfg.lr_vision_backbone > 0:\n",
        "                    adj_lr_vision_backbone = get_lr(global_step, train_cfg.lr_vision_backbone, train_cfg.max_training_steps)\n",
        "                    optimizer.param_groups[param_group_idx]['lr'] = adj_lr_vision_backbone\n",
        "                    param_group_idx += 1\n",
        "\n",
        "                if train_cfg.lr_language_backbone > 0:\n",
        "                    adj_lr_language_backbone = get_lr(global_step, train_cfg.lr_language_backbone, train_cfg.max_training_steps)\n",
        "                    optimizer.param_groups[param_group_idx]['lr'] = adj_lr_language_backbone\n",
        "              \n",
        "                optimizer.step()\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "            batch_loss = loss.item()\n",
        "            if train_cfg.gradient_accumulation_steps > 1:\n",
        "                batch_loss = batch_loss * train_cfg.gradient_accumulation_steps\n",
        "            total_train_loss += batch_loss\n",
        "            batch_losses.append(batch_loss)\n",
        "\n",
        "            num_tokens = torch.sum(attention_mask).item() # Sum of attention mask gives number of tokens\n",
        "            total_tokens_processed += num_tokens\n",
        "\n",
        "            batch_end_time = time.time()\n",
        "            batch_duration = batch_end_time - batch_start_time\n",
        "            tokens_per_second = num_tokens / batch_duration\n",
        "\n",
        "            if global_step % 20 == 0:\n",
        "                model.eval()\n",
        "                torch.cuda.empty_cache()  # Clear GPU memory\n",
        "                with torch.no_grad():\n",
        "                    total_val_loss = 0\n",
        "                    for batch in synchronized_dataloader_step(val_loader, False):\n",
        "                        images = batch[\"images\"]\n",
        "                        input_ids = batch[\"input_ids\"].to(device)\n",
        "                        labels = batch[\"labels\"].to(device)\n",
        "                        attention_mask = batch[\"attention_mask\"].to(device)\n",
        "\n",
        "                        with torch.amp.autocast(device_type='cuda', dtype=torch.float16):\n",
        "                            _, loss = model(input_ids, images, attention_mask=attention_mask, targets=labels)\n",
        "\n",
        "                        total_val_loss += loss.item()\n",
        "                    avg_val_loss = total_val_loss / len(val_loader)\n",
        "                    val_losses.append(avg_val_loss)\n",
        "                    val_plot_steps.append(global_step)\n",
        "                print(f\"\\nStep: {global_step}, Loss: {batch_loss:.4f}, Val Loss: {avg_val_loss:.4f}, Tokens/s: {tokens_per_second:.2f}\")\n",
        "                model.train()\n",
        "\n",
        "            global_step += 1\n",
        "\n",
        "        avg_train_loss = total_train_loss / len(train_loader)\n",
        "\n",
        "        epoch_end_time = time.time()\n",
        "        epoch_duration = epoch_end_time - epoch_start_time\n",
        "        epoch_times.append(epoch_duration)\n",
        "\n",
        "        epoch_tokens_per_second = total_tokens_processed / epoch_duration\n",
        "\n",
        "        print(f\"Epoch {epoch} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | Time: {epoch_duration:.2f}s | T/s: {epoch_tokens_per_second:.2f}\")\n",
        "\n",
        "    model.save_pretrained(save_directory=vlm_cfg.vlm_checkpoint_path)\n",
        "    model.push_to_hub(hf_model_name)\n",
        "\n",
        "    total_training_time = sum(epoch_times)\n",
        "    print(f\"Total training time: {total_training_time:.2f}s\")\n",
        "\n",
        "    plt.plot(batch_losses, label='Train Loss')\n",
        "    plt.plot(val_plot_steps, val_losses, label='Val Loss')\n",
        "    plt.xlabel('Batch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.title('Loss Curve')\n",
        "    plt.grid(True)\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d4HmsEPNQZbh",
      "metadata": {
        "id": "d4HmsEPNQZbh"
      },
      "source": [
        "### Prepare the Configs\n",
        "Instead of using the config.py file in the repo (which was created to run on one H100), we will create our config here to play around with the parameters easier and adapt them to colabs capabilities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "h8FlqtizQdO-",
      "metadata": {
        "id": "h8FlqtizQdO-"
      },
      "outputs": [],
      "source": [
        "@dataclass\n",
        "class VLMConfig:\n",
        "    vit_hidden_dim: int = 768\n",
        "    vit_inter_dim: int = 4 * vit_hidden_dim\n",
        "    vit_patch_size: int = 16\n",
        "    vit_img_size: int = 512\n",
        "    vit_n_heads: int = 12\n",
        "    vit_dropout: float = 0.0\n",
        "    vit_n_blocks: int = 12\n",
        "    vit_ln_eps: float = 1e-6\n",
        "    vit_cls_flag: bool = False\n",
        "    vit_model_type: str = 'google/siglip2-base-patch16-512'\n",
        "\n",
        "    lm_hidden_dim: int = 960\n",
        "    lm_inter_dim: int = 2560\n",
        "    lm_rms_eps: float = 1e-5\n",
        "    lm_re_base: int = 100000\n",
        "    lm_max_position_embeddings: int = 8192\n",
        "    lm_base_vocab_size: int = 49152\n",
        "    extra_token_amount: int = 66  # Number of extra tokens for the VLM (image start, image end, image token)\n",
        "    lm_vocab_size: int = lm_base_vocab_size + extra_token_amount # Not a great way to do this, but it works for now (vlm_extra_tokens cannot be a dict, since this is mutable, and a Field has no len() function)\n",
        "    lm_n_heads: int = 15\n",
        "    lm_n_kv_heads: int = 5\n",
        "    lm_dropout: float = 0.0\n",
        "    lm_n_blocks: int = 32\n",
        "    lm_attn_scaling: float = 1.0\n",
        "    lm_max_length: int = 256\n",
        "    lm_use_tokens: bool = False # Decide if the LM expects tokens or embeddings as input (if using as a backbone for the VLM, set to False)\n",
        "    lm_tie_weights: bool = True # Decide if you want to tie the LM Head weight to the token embedding weights\n",
        "    lm_model_type: str = 'HuggingFaceTB/SmolLM2-135M'\n",
        "    lm_tokenizer: str = 'HuggingFaceTB/SmolLM2-360M-Instruct'\n",
        "    lm_chat_template: str = \"{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}\"\n",
        "\n",
        "    mp_pixel_shuffle_factor: int = 4\n",
        "    mp_image_token_length: int = 64\n",
        "\n",
        "    max_img_size: int = 512\n",
        "    resize_to_max_side_len: bool = False\n",
        "\n",
        "    vlm_extra_tokens: dict[str, str] = field(default_factory=lambda: {\"image_token\": \"<|image|>\", \"global_image_token\": \"<|global_image|>\",\n",
        "      \"r1c1\": \"<row_1_col_1>\", \"r1c2\": \"<row_1_col_2>\", \"r1c3\": \"<row_1_col_3>\", \"r1c4\": \"<row_1_col_4>\", \"r1c5\": \"<row_1_col_5>\", \"r1c6\": \"<row_1_col_6>\", \"r1c7\": \"<row_1_col_7>\", \"r1c8\": \"<row_1_col_8>\",\n",
        "      \"r2c1\": \"<row_2_col_1>\", \"r2c2\": \"<row_2_col_2>\", \"r2c3\": \"<row_2_col_3>\", \"r2c4\": \"<row_2_col_4>\", \"r2c5\": \"<row_2_col_5>\", \"r2c6\": \"<row_2_col_6>\", \"r2c7\": \"<row_2_col_7>\", \"r2c8\": \"<row_2_col_8>\",\n",
        "      \"r3c1\": \"<row_3_col_1>\", \"r3c2\": \"<row_3_col_2>\", \"r3c3\": \"<row_3_col_3>\", \"r3c4\": \"<row_3_col_4>\", \"r3c5\": \"<row_3_col_5>\", \"r3c6\": \"<row_3_col_6>\", \"r3c7\": \"<row_3_col_7>\", \"r3c8\": \"<row_3_col_8>\",\n",
        "      \"r4c1\": \"<row_4_col_1>\", \"r4c2\": \"<row_4_col_2>\", \"r4c3\": \"<row_4_col_3>\", \"r4c4\": \"<row_4_col_4>\", \"r4c5\": \"<row_4_col_5>\", \"r4c6\": \"<row_4_col_6>\", \"r4c7\": \"<row_4_col_7>\", \"r4c8\": \"<row_4_col_8>\",\n",
        "      \"r5c1\": \"<row_5_col_1>\", \"r5c2\": \"<row_5_col_2>\", \"r5c3\": \"<row_5_col_3>\", \"r5c4\": \"<row_5_col_4>\", \"r5c5\": \"<row_5_col_5>\", \"r5c6\": \"<row_5_col_6>\", \"r5c7\": \"<row_5_col_7>\", \"r5c8\": \"<row_5_col_8>\",\n",
        "      \"r6c1\": \"<row_6_col_1>\", \"r6c2\": \"<row_6_col_2>\", \"r6c3\": \"<row_6_col_3>\", \"r6c4\": \"<row_6_col_4>\", \"r6c5\": \"<row_6_col_5>\", \"r6c6\": \"<row_6_col_6>\", \"r6c7\": \"<row_6_col_7>\", \"r6c8\": \"<row_6_col_8>\",\n",
        "      \"r7c1\": \"<row_7_col_1>\", \"r7c2\": \"<row_7_col_2>\", \"r7c3\": \"<row_7_col_3>\", \"r7c4\": \"<row_7_col_4>\", \"r7c5\": \"<row_7_col_5>\", \"r7c6\": \"<row_7_col_6>\", \"r7c7\": \"<row_7_col_7>\", \"r7c8\": \"<row_7_col_8>\",\n",
        "      \"r8c1\": \"<row_8_col_1>\", \"r8c2\": \"<row_8_col_2>\", \"r8c3\": \"<row_8_col_3>\", \"r8c4\": \"<row_8_col_4>\", \"r8c5\": \"<row_8_col_5>\", \"r8c6\": \"<row_8_col_6>\", \"r8c7\": \"<row_8_col_7>\", \"r8c8\": \"<row_8_col_8>\"})\n",
        "    vlm_load_backbone_weights: bool = True\n",
        "    vlm_checkpoint_path: str = 'checkpoints'\n",
        "    hf_repo_name: str = 'nanoVLM'\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class TrainConfig:\n",
        "    lr_mp: float = 0.005\n",
        "    lr_vision_backbone: float = 0.0005\n",
        "    lr_language_backbone: float = 0.0005\n",
        "    data_cutoff_idx: int = 128 # Let's only use a small subset at first\n",
        "    val_ratio: float = 0.2\n",
        "    batch_size: int = 1\n",
        "    gradient_accumulation_steps: int = 4\n",
        "    max_grad_norm: float = 1.0\n",
        "    max_training_steps: int = 200\n",
        "    max_images_per_example: int = 2\n",
        "    max_images_per_knapsack: int = 8\n",
        "    max_sample_length: int = 256\n",
        "    compile: bool = False\n",
        "    resume_from_vlm_checkpoint: bool = False # Indicate if the training should be resumed from a checkpoint of the whole VLM or you want to start from scratch\n",
        "    train_dataset_path: str = 'HuggingFaceM4/the_cauldron'\n",
        "    train_dataset_name: tuple[str, ...] = (\"tqa\", ) #All options; (\"ai2d\", \"aokvqa\", \"chart2text\", \"chartqa\", \"clevr\", \"cocoqa\", \"datikz\", \"diagram_image_to_text\", \"docvqa\", \"dvqa\", \"figureqa\", \"finqa\", \"geomverse\", \"hateful_memes\", \"hitab\", \"iam\", \"iconqa\", \"infographic_vqa\", \"intergps\", \"localized_narratives\", \"mapqa\", \"multihiertt\", \"ocrvqa\", \"plotqa\", \"raven\", \"rendered_text\", \"robut_sqa\", \"robut_wikisql\", \"robut_wtq\", \"scienceqa\", \"screen2words\", \"st_vqa\", \"tabmwp\", \"tallyqa\", \"tat_qa\", \"textcaps\", \"textvqa\", \"tqa\", \"vistext\", \"visual7w\", \"visualmrc\", \"vqarad\", \"vqav2\", \"vsr\", \"websight\") # \"clevr_math\", \"okvqa\", \"spot_the_diff\", \"nlvr2\", \"mimic_cgd\","
      ]
    },
    {
      "cell_type": "markdown",
      "id": "KmFQwKGcSLr_",
      "metadata": {
        "id": "KmFQwKGcSLr_"
      },
      "source": [
        "### Lets run the training!"
      ]
    },
    {
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      "id": "9MlFpXQFSNdx",
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        },
        "id": "9MlFpXQFSNdx",
        "outputId": "c2215627-473d-4a7e-ab66-00ba73da423b"
      },
      "outputs": [],
      "source": [
        "vlm_cfg = VLMConfig()\n",
        "train_cfg = TrainConfig()\n",
        "train(train_cfg, vlm_cfg)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "78d938dd",
      "metadata": {},
      "source": [
        "As you can see the model trains, so feel free to play around with the architecture or data! Let us know what you build with it!\n",
        "\n",
        "PS: If you want to test the model, check out generate.py to see how to do inference with it"
      ]
    }
  ],
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